Accurate and quantitative single cell sequencing with digital droplet MDA PI: Adam R. Abate Abstract: Accurate single cell genomic sequencing is important for applications ranging from copy number variation analysis in cancer to the investigation of uncultivable microbes. To provide sufficient DNA for sequencing, a single cell's genome must be massively amplified. However, existing methods introduce errors (MALBAC) or result in significant bias (MDA), yielding low-confidence genomes with gaps in coverage. In this proposal, we will develop single cell digital droplet MDA (sc-ddMDA), a method that will combine the uniform amplification of MALBAC with the high copying accuracy of MDA. This project will extend our preliminary results in which we have demonstrated the ability to reliably and uniformly amplify the genomes of just ten bacterial cells. Using novel biochemical and microfluidic methods, we will push the limit to a single cell and will implement automation for high-throughput processing of, ultimately, thousands of single cells. Importantly, we will provide two versions of the technology, one using no specialized equipment accessible to any lab with molecular biology expertise, and a higher-throughput version using microfluidics and robotic automation. Aim 1: Optimize methods to uniformly amplify and sequence the genomes of single cells. Aim 2: Develop high-throughput single cell ddMDA. Aim 3: Implement microfluidic pre-amplification for accurate, high-throughput single cell sequencing.